Source: Gartner's report What's Hot in CRM Applications in 2013 - covers marketing, sales, customer service and e-commerce. Big data, cloud, social, mobile and the Internet of Things are the five catalysts that are driving inquiries in the hottest areas of interest.

Part I

Part II

University of Colorado Denver - Tuesday June 25, 2013

Using Python for Data Logistics - Abstract

Big Data and Data Science projects looking to reduce the risks, costs, and nightmares associated with managing dozens of data feeds have discovered the ETL (Extract, Transform, Load) product category. But there's no such thing as a silver bullet, and while there are practices and lessons to be learned from ETL, the tools are mostly the legacy of early 90s thinking in which data feeds were fewer, the alternative were COBOL or C, and writing code was deemed risky by DBAs and management. Ken will show how a high-level language like Python, when matched with certain practices and design patterns can offer a very successful alternative to these diagram-driven development tools. The discussion will focus on concepts, designs and patterns, and will include examples of successes and failures with a small amount of code.

Bio

Ken Farmer is a data architect at IBM where he has built and led their Security & Compliance Data Warehouses. These projects used Python extensively for systems management, general data management, ETL, and analytics. He writes about data management at www.ken-far.com, and writes data analysis tools like DataGristle for fun on the side.

With the Emergence of Big Data, Where do Relational Technologies Fit? - Abstract

The recent focus on Big Data in the data management community brings with it a paradigm shift—from the more traditional top-down, "design then build" approach to data warehousing and business intelligence, to the more bottom up, "discover and analyze" approach to analytics on Big Data. Where do relational data bases, data modeling, and data warehousing fit in this new world of Big Data? Do they go away, or can they evolve to meet the emerging needs of this exciting new technology? Join industry expert Donna Burbank as she discusses the issues and opportunities that exist for data management professionals in the Big Data environment.

Bio

Donna Burbank is a recognized industry expert and author, with more than 15 years of experience in data management, metadata management, and enterprise architecture. Donna currently is VP of product marketing for CA Technologies' data modeling solutions. Previous to this role, she has served in key brand strategy and product management roles at Computer Associates and Embarcadero Technologies and as a senior consultant for PLATINUM technology's information management consulting division in both the U.S. and EMEA. She has worked with dozens of Fortune 500 companies worldwide in the U.S., Europe, Asia, and Africa and speaks regularly at industry conferences. She has recently co-authored two books:

Smartphone OEM Market Share138.5 million people in the U.S. owned smartphones (58 percent mobile market penetration) during the three months ending in April, up 7 percent since January. Apple ranked as the top OEM with 39.2 percent of U.S. smartphone subscribers (up 1.4 percentage points from January). Samsung ranked second with 22 percent market share (up 0.6 percentage points), followed by HTC with 8.9 percent, Motorola with 8.3 percent and LG with 6.7 percent.

An infographic about how IT departments think of big data. For this infographic, Infochimps interviewed 300 IT personnel of which over 50% were involved with big data initiatives. Click here or image to enlarge.

By developing a data science and predictive analytics strategy for marketing, organizations identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and identify the true drivers of financial performance. Data science helps uncover new business opportunities and delivers new insights that help an organization increase marketing campaign effectiveness, maximize customer and product profitability, minimize customer churn, and detect fraud.

Data science and predictive analytics can help marketing pros know where to find the new revenue opportunities and which product or service offerings are most likely to address the market requirement. The goal is to leverage both internal and external data - as well as structured and unstructured data - to gain competitive advantage and make better decisions. To reach this goal an organization needs access to professional data scientists and new data analytical technologies.

Data science refers to the scientific study of the creation, manipulation and transformation of data to create meaning. Business analytics is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. Predictive analytics turns data into valuable, actionable information. Predictive analytics uses data to determine the probable future outcome of an event or a likelihood of a situation occurring and encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

An example of using predictive analytics is optimizing customer relationship management (CRM) systems. They can help enable an organization to analyze all customer data therefore exposing patterns that predict customer behavior. Another example is for an organization that offers multiple products, predictive analytics can help analyze customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships.

A recent study from the Economist Intelligence Unit (EIU) suggests that business leaders rate creating a data strategy for marketing and predictive analytics as one of the most important priorities. Regarding marketing skills, 37% of executives said that "using data analysis to extract predictive findings from 'big data'" is most important. Yet marketing pros often lack the data science and business analytics skills to get valuable, actionable insights from both large and small data sets. Further, many organizations lack an information, technology and data science strategy.

"This change in the required skill set ... has created a challenge for marketers as 45% of executives now view marketers' limited competency in data analysis as a major obstacle to implementing more effective strategies -- second only to inadequate budgets for digital marketing and database management." The EIU report also suggests that firms are underestimating their customers' privacy concerns: 33% of consumers say they are "very concerned" about the privacy of their information stored in companies' databases yet only 23% of executives say their organization's customers are very concerned about privacy.

The solution is to engage expert professionals to help develop an information, technology and data science strategy. Further, training marketing pros to use technology, work with data scientists (both internal and external) and developing business analytics skills to get valuable, actionable insights from data is critical.

Hiring both internal and external data scientists is optimal. Internal data scientists can develop specific domain expertise and help train staff. External data scientists can bring objectivity and fresh perspectives to business challenges as well as create a check and balance or auditing function.

If an organization is unable to hire internal data scientists, engaging external data scientists is a viable option to quickly form a data science team and scale-up big data projects without the upfront CapEx of hiring data scientists in-house. Knowledge transfer and training may be included. Data scientists can be engaged on a time or fixed fee basis and be responsible for deploying, managing and scaling the data science and predictive analytics projects.

Robin Hanson is an expert on prediction markets and the social implications of future technologies (e.g., artificial intelligence and nano-technology and their influence on the economy and society). Politically he supports futarchy -- a society where policy decisions are made based on open prediction markets.

Robin has a diverse background: he has a Bachelor's and Master's degree in physics but a PhD in social sciences. Robin has researched the artificial intelligence and Bayesian statistics at NASA and developed a prediction market predicting the political stability, economic growth and military activity of foreign countries for the US Department of Defense.

Robin's ideas often cause controversial reactions and he wishes to find ways to more effectively decentralize the government's tasks. Below are some interesting thoughts:

AI takes software, not just hardware. It is tempting to project when artificial intelligence (AI) will arrive by projecting when a million dollars of computer hardware will have a computing power comparable to a human brain. But AI needs both hardware and software. It might be that when the software is available, AI will be possible with today’s computer hardware.

AI software progress has been slow. My small informal survey of AI experts finds that they typically estimate that in the last 20 years their specific subfield of AI has gone ~5-10% of the way toward human level abilities, with no noticeable acceleration. At that rate it will take centuries to get human level AI.

Emulations might bring AI software sooner. Human brains already have human level software. It should be possible to copy that software into computer hardware, and it seems likely that this will be possible within a century.

Emulations would be sudden and human-like. Since having an almost emulation probably isn’t of much use, emulations can make for a sudden transition to a robot economy. Being copies of humans, early emulations are more understandable and predictable than robots more generically, and many humans would empathize deeply with them.

Growth rates would be much faster. Our economic growth rates are limited by the rate at which we can grow labor. Whether based on emulations or other AI, a robot economy could grow its substitute for labor much faster, allowing it to grow much faster (as in an AK growth model). A robot economy isn’t just like our economy, but with robots substituted for humans. Things would soon change very fast.

There probably won’t be a grand war, or grand deal. The past transitions from foraging to farming and farming to industry were similarly unprecedented, sudden, and disruptive. But there wasn’t a grand war between foragers and farmers, or between farmers and industry, though in particular wars the sides were somewhat correlated. There also wasn’t anything like a grand deal to allow farming or industry by paying off folks doing things the old ways. The change to a robot economy seems too big, broad, and fast to make grand overall wars or deals likely, though there may be local wars or deals.